Search Results (3,128)

This study presents a SCAPS-1D-based numerical optimization of an organic ultraviolet (UV) photodetector employing an FTO/PTB7/Spiro-OMeTAD/Au device architecture. The novelty of this work lies in a simulation-guided, UV-specific optimization strategy that combines thickness engineering, controlled doping, and contact work-function tuning to achieve intrinsic spectral selectivity without external optical filters. We systematically optimize material and device parameters, including active layer thicknesses, donor and acceptor densities, and the metal electrode work function, to enhance responsivity, detectivity, and spectral performance. Simulations identify optimal thicknesses of 1200 nm for PTB7 and 1000 nm for Spiro-OMeTAD, with donor concentrations of 1 × 10²⁰ cm⁻³ and 1 × 10¹⁸ cm⁻³, respectively. A comparative contact analysis demonstrates that replacing aluminum with gold (Au) forms a near-ohmic back contact, leading to improved hole extraction and suppressed dark current due to favorable energy-level alignment. The optimized device achieves a peak external quantum efficiency of approximately 80% in the 300–400 nm ultraviolet range, with a responsivity up to 0.4 A/W. The UV selectivity originates from the absorption characteristics of PTB7 combined with suppressed long-wavelength charge collection, resulting in a negligible response in the visible–near-infrared region. These results confirm the device’s strong potential for high-sensitivity, solar-blind UV photodetection. By integrating practical material selection with physically consistent SCAPS-1D optoelectronic modeling, this work provides a robust design framework to guide the development of next-generation organic UV photodetectors for environmental sensing, biomedical diagnostics, and wearable optoelectronics. Full article

Background and Aims: Neurovascular abnormalities, such as aberrant nerve migration in Hirschsprung’s disease and reduced vascular density in necrotizing enterocolitis, are frequently observed in intestinal diseases. Traditional 2-dimensional (2D) staining methods are complicated, time-consuming and fail to comprehensively visualize the intricate neurovascular structures and morphology of the intestine. This study focuses on evaluating a novel 3D staining technique that promises simpler, faster, and more effective visualization of intact neurovascular structures in the colon. Additionally, it aims to compare the strengths and limitations of this 3D method against traditional 2D techniques for analyzing neuronal and vascular changes in two prevalent pathological conditions. Methods: A novel tissue-clearing approach was used to render mouse and patient distal colon tissues transparent. Neural structures and blood vessels were stained. 2D and 3D imaging were performed with laser confocal or tiling light sheet microscopy. Parameters include total imaging time, imaging range, image quality, operational complexity, and post-processing were compared between 2D and 3D methods. Results: Compared to 2D imaging, 3D imaging reveals the complete morphology and trajectory of neurovascular structures. Confocal 3D imaging offers superior clarity, higher transparency, and faster workflow efficiency, whereas light-sheet microscopy provides broader coverage at the expense of lower image quality. Post-processing facilitated spatial modeling and quantitative analyses. Applications included Hirschsprung’s disease, where 3D imaging revealed abnormal nerve distribution, and congenital heart disease, where hypoperfusion impacted vascular development in the colon. Conclusions: Confocal 3D staining and imaging offered a more streamlined workflow and enabled comprehensive visualization of neurovascular architecture, supporting efficient assessment of intestinal neurovascular phenotypic features. Full article

Objectives: This study aimed to identify factors influencing the magnitude of the difference between plasma glucose concentration (Glu(P)) and serum glucose concentration (Glu(S)). Methods: A total of 333 healthy Japanese adults aged 22–29 years (212 males and 121 females) were enrolled. Plasma samples were collected using glycolytic inhibitors, whereas serum samples were obtained without glycolytic inhibitors and kept at room temperature. Glu(P) and Glu(S) were measured and compared. Results: The median difference between Glu(P) and Glu(S), defined as Glu(P-S), was 4 mg/dL across all participants, with no gender-related differences. A strong positive correlation was observed between Glu(P) and Glu(S). Glu(P-S) was positively correlated with body mass index, Glu(P), triglyceride–glucose index, white blood cell count, serum sodium, magnesium, and zinc levels. In contrast, Glu(P-S) was negatively correlated with Glu(S), hemoglobin A1c (HbA1c), homeostasis model assessment of beta-cell function, and high-density lipoprotein cholesterol (HDL-C). Multiple regression analysis demonstrated that HDL-C and HbA1c were independent determinants of Glu(P-S) in the overall cohort. Among females, HDL-C, triglyceride, low-density lipoprotein cholesterol, ferritin, and C-reactive protein independently influenced Glu(P-S), whereas no independent determinants were identified in males. Conclusions: Plasma glucose concentrations measured with glycolytic inhibitors were significantly higher than serum glucose concentrations measured without inhibitors at room temperature. The magnitude of Glu(P-S) appears to be associated with markers of insulin resistance, particularly HDL-C levels. Full article

Background/Objectives: 3D printing, particularly fused deposition modeling (FDM), is an emerging technology in pharmaceutical manufacturing, enabling the customization of dose or release rate to individual patient needs. However, finding the appropriate loading method to ensure the stability of the drug and achieve the targeted dose may be challenging. Furthermore, the drug utilization of most loading methods is poor, which results in considerable waste production and increased environmental burden. This study aimed to compare two post-printing drug-loading techniques: electronic syringe deposition and pan coating on FDM-printed polylactic acid (PLA) tablets. PLA is a biodegradable and biocompatible polymer that is widely used in this field due to its mechanical strength and regulatory approval. Methods: Tablets with honeycomb-shaped infill (30% and 60% infill densities) were fabricated using PLA filaments, followed by loading with a 15% paracetamol solution via either electronic syringe deposition or pan coating. The resulting tablets were assessed for drug content, weight variation, friability%, surface morphology (SEM), drug distribution (Raman mapping), solid-state characteristics (DSC and FTIR), and dissolution performance. Results: The results indicated that pan coating and electronic syringe deposition offered drug utilization up to 88% and 91.7%, respectively, which is superior to conventional soaking methods. Nevertheless, there is a significant difference in drug loading and release rate: pan coating yielded up to 10.14% drug loads and fast release (over 80% in 30 min), while electronic syringe deposition showed lower drug loading up to 4.8% and slower release (less than 80% within 60 min), which could be associated with better mechanical film integrity and higher precision. Both methods met USP standards with a weight loss of less than 1% and maintained the drug’s crystalline state and compatibility with PLA. Conclusions: FDM combined with controlled post-printing drug loading presents a rapid, cost-effective, and flexible novel approach for manufacturing personalized immediate-release tablets, with pan coating potentially being more suitable for commercial scalability and electronic syringe offering precise dosing for personalized therapies. Full article

Gas turbines play a critical role in modern power systems, yet their transient operations (e.g., start-up, load mutation) induce significant thermal inertia in metal components, leading to deviations between simulation results and actual performance. Traditional low-dimensional (1D/0D) simulation models sacrifice detailed flow and temperature field information to reduce computational load, while high-dimensional (3D) computational fluid dynamics (CFD) models are impractical for full-system simulations due to excessive computational costs. This discrepancy creates a critical trade-off between simulation accuracy and efficiency in gas turbine thermal inertia studies. To address this challenge, this study proposes a temperature-gradient-guided dynamic genetic optimization sampling algorithm (TDGA) and integrates it into a multi-dimensional data scaling framework for gas turbines. A fully coupled simulation framework was established, combining 3D CFD models for turbine flow paths (resolving detailed flow and temperature fields) and 1D thermal models for metal components (casing, hub, blades). The TDGA was designed to enable efficient data interoperability between models: it incorporates a dynamic encoding mechanism, temperature gradient weight matrix, density penalty term, quantity penalty term, and regularization term to optimize sampling point distribution. Dynamic weight coefficients for each objective function term and adaptive crossover/mutation probabilities were introduced to balance global exploration (early iterations) and local exploitation (late iterations) during optimization. Comparative analysis showed that the TDGA achieved a mean squared error (MSE) of 15.52K, far lower than those of traditional Latin Hypercube Sampling (75.07K) and Bootstrap Sampling (64.38K). It allocated 70.11% of sampling points to high-temperature gradient regions while reducing the total number of sampling points to 2765. During the middle stage of the gas turbine start-up process, compared with the traditional Latin Hypercube Sampling and Bootstrap Sampling, the average error of the proposed sampling algorithm is reduced by 17.4% and 13.3%, respectively. The proposed TDGA-based framework effectively balances simulation accuracy and computational efficiency, providing a reliable approach for the transient thermal analysis of gas turbines. Full article

This paper investigates kernel estimation of the error density function for the two-phase linear regression model. We derive the asymptotic distributions of residual-based kernel density estimators. First, we demonstrate that the asymptotic distribution of the maximum deviation (suitably normalized) between the residual-based kernel density estimator and the expected kernel density (based on the true errors) coincides with the result for an independent and identically distributed (i.i.d.) sample, as established in Bickel and Rosenblatt (1973). We then prove that the residual-based kernel density estimator is asymptotically normal at a fixed point. Full article

Laminar separation bubbles (LSBs) on low-Reynolds-number airfoils are sustained by intrinsic unsteadiness driven by Kelvin–Helmholtz (K-H) growth in the separated shear layer. Using incompressible 2D URANS with the SA-$\gamma$ transition model for a NACA 0012 airfoil at $Re=5.3\times {10}^4$, we reveal that numerical dissipation behaves as a critical bifurcation parameter. Validated against the recent Jardin (2025) experimental benchmark, the physical state correctly resolves the LSB-induced pressure plateau ($C_p$) and local negative skin friction ($C_f<0$). However, when numerical dissipation exceeds the K-H instability growth rate, the physical limit-cycle oscillation collapses into a spurious fixed-point attractor—a phenomenon defined as numerical quenching. This pseudo-convergence triggers a catastrophic ∼30% deficit in mean lift ($C_l$). Furthermore, at $\alpha =6^{\circ }$, a drag-mechanism inversion is identified: while the physical branch is dominated by LSB-induced pressure (form) drag, the quenched branch exhibits a non-physical drag surge that exceeds the fully turbulent baseline. Phase portraits and power spectral densities ($St\approx 0.2$) provide objective diagnostics, demonstrating that standard residual convergence is a deceptive indicator of physical fidelity in transitional separated aerodynamics. Full article

This work introduces an integrated Material Point Method (MPM) framework for optimizing tendon-driven hyperelastic robots under extreme 3D deformations. To overcome the mesh distortion limitations of the traditional FEM at large strains, we develop a coupled MPM–tendon hyperelastic model that integrates Yeoh constitutive laws with discrete tendon actuation forces. The model enables robust simulation of anisotropic stress propagation through Lagrangian particle tracking and Eulerian grid discretization, eliminating mesh entanglement artifacts. A strain-gradient-driven tendon path algorithm ensures mechanical efficiency using Fréchet distance-based similarity metrics and curvature smoothness screenin, enforcing spatial continuity in complex topologies. Validation demonstrates: (1) Sub 3 mm geometric errors and about 89% volumetric overlap in worm-inspired deformations; (2) optimal computational efficiency at 0.4–0.6 mm grid densities, balancing accuracy and resource overhead; and (3) projected alignment errors of 0.8 mm (XY), 1.3 mm (XZ), and 2.9 mm (YZ) in multi-view spatial analyses. The framework achieves about 89% ± 2% volumetric overlap in quadrupedal morphing via agonist–antagonist tendon optimization, demonstrating efficacy for extreme 3D deformation control. Full article

To address the challenges of traditional methods for monitoring rock dynamic hazards in mines, which struggle to fully characterize the spatiotemporal heterogeneity of damage evolution and the resulting lag in early warning, this paper proposes a dynamic rock damage classification and fracture early warning model driven by acoustic emission data. Based on an improved dynamic K-means algorithm, this model fuses time dependence, energy intensity, and event spatial density characteristics through exponentially decaying weights to construct a spatiotemporal-energy synergistic clustering framework. Furthermore, a nonlinear coupling model for the comprehensive risk index (RI) is established, combining the static damage variable D with dynamic parameters such as energy release rate, ring count, and spatial clustering, to create a five-level early warning threshold. Experimental results demonstrate that the improved algorithm achieves clustering silhouette coefficients exceeding 0.7 for single-source, multi-source, and complex fracture patterns, and the error between cluster regions and actual fracture distribution is less than 1 mm. The RI model accurately identifies the damage state of the test block and effectively predicts critical instability, significantly improving both timeliness and accuracy. This research overcomes the limitations of traditional static evaluation and provides high-precision technical support for real-time monitoring of hidden rock fractures and prevention and control of mine dynamic hazards. Full article

Urban expansion and spatial restructuring significantly influence the urban thermal environment. This study investigates the central urban area of Jinan, developing a multi-dimensional spatial structure index system that integrates terrain, 2D/3D morphology, and layout based on multi-source data. Land surface temperature (LST) was derived from remote sensing imagery. Using road networks and triangulated irregular networks (TINs) generated from a digital elevation model (DEM), hybrid analysis units were created. Pearson correlation and bivariate global/local spatial autocorrelation analyses were applied to examine the mechanisms and spatial heterogeneity of how urban spatial structure affects LST. The results showed that (1) LST was strongly associated with urban spatial structure. Among the 12 significantly correlated indicators, building density showed the strongest positive correlation with LST (r = 0.5883), while DEM mean had the strongest negative correlation (r = −0.7444), indicating that compact built-up areas intensified heating, whereas terrain most strongly moderated surface temperature. (2) LST and indicator correlations varied with elevation. LST showed a negative correlation with the standard deviation of DEM, suggesting that greater terrain variability enhances cooling effects. This spatial variation in the dominant drivers of the thermal environment reflects a clear divergence of influencing factors across different elevational zones. The thermal environment exhibits a pronounced north–south split: cooling effects prevail in the south due to terrain, while warming effects dominate in the north due to building forms. (3) Bivariate spatial autocorrelation revealed clear spatial heterogeneity. High–high clustering of LST and spatial structure indicators in the northern plain denoted heat-aggregated zones. Low–low clustering in the topographically complex, sparsely built south formed cold-source zones, and transitional areas showed mixed high–low and low–high clustering. (4) Based on these findings, a zonal governance framework was advocated, prioritizing terrain assessment followed by spatial structure optimization. This promoted a shift from uniform to precise, zone-based thermal environment management, laying a scientific foundation for sustainable spatial planning. Full article

Covalent organic frameworks (COFs) have emerged as promising candidates for solid-state electrolytes (SSEs) in lithium-ion batteries (LIBs) due to their tunable pore sizes, high surface areas, and exceptional thermal stability. However, the rational design of COF-based SSEs is hindered by the vast combinatorial chemical space, synthetic complexity, and the need for precise control over structure-property relationships. Machine learning (ML) has revolutionized the development of COF materials by enabling high-throughput screening, predictive modeling, and optimization of synthesis conditions. This review systematically explores the integration of ML in COF-based SSE development, focusing on structure prediction, synthesis-performance optimization, and the application of digital twin strategies. We highlight the role of ML in accelerating the discovery of high-performance COF-based solid-state electrolytes, optimizing ionic conductivity, and enhancing interfacial stability. By summarizing the synergistic pathways between computational simulations and experimental validation, this review offers strategic guidelines for overcoming traditional “trial-and-error” R&D bottlenecks, paving the way for the next generation of high-energy-density LIBs. Full article

An integrated nonlinear dynamic model was developed to investigate resonance in a four-cylinder engine mounted on a viscoelastic foundation. A coupled lumped-parameter formulation captures vertical and torsional responses under unbalanced inertial forces, combustion torque, and stochastic base excitation. Time-domain simulations show that at low rotational speeds the vertical displacement reaches transient amplitudes before converging to periodic oscillations, whereas higher excitation speeds reduce steady-state amplitudes. Torsional motion exhibits initial angles near 0.05 rad that decay below 0.01 rad in steady state, with further reduction at higher speeds. Frequency-domain analysis indicates that vibration energy is concentrated in engine-order harmonics between approximately 8 and 50 Hz, while components above 60 Hz are strongly attenuated, yielding a dynamic range exceeding 50 dB. Finite element modal analysis identifies the first four structural modes between 18 Hz and 666 Hz, revealing an increasingly dominant overall translational mode and a localized directional behavior at higher frequencies. A high-dimensional kernel density spectrogram integrates modal and spectral features to map resonance regions. Results indicate that increasing rotational excitation enhances inertial stiffening, systematically reduces displacement amplitudes, and preserves bounded periodic dynamics without instability. Full article

Mild fasting hypertriglyceridemia is often accompanied by early insulin resistance and atherogenic dyslipidemia, making it an attractive target for pragmatic dietary prevention. This trial aims to determine whether substituting common cereal-based staples with functional low-glycemic index (low-GI) products improves the triglyceride–glucose (TyG) index in adults with fasting triglycerides >150 mg/dL. The GLOW study is an exploratory, randomized, double-blind, single-center crossover trial. Adults aged ≥18 years with fasting triglycerides >150 mg/dL and body mass index ≤30 kg/m² will be enrolled. Participants will follow a stabilized Mediterranean-style diet and will complete two 28-day intervention periods in random sequence: (i) functional low-GI Altograno^® pasta, pizza base and flatbread; and (ii) conventional standard wholegrain products. Intervention periods will be separated by a 28-day washout. Study foods will be consumed as fixed daily substitutions of usual staple servings (one bread portion and one pasta or pizza portion). The primary endpoint is the between-intervention difference in TyG response over each period, defined as the period-specific change from the corresponding period baseline to the end-of-period assessment. The primary analysis will compare end-of-period TyG between interventions while adjusting for the period-specific baseline value. Secondary endpoints include fasting triglycerides and glucose, atherogenic lipoproteins (non–high-density lipoprotein cholesterol and apolipoprotein B), inflammation (high-sensitivity C-reactive protein), endothelial reactivity assessed with the Endocheck^®/Vicorder^® system, and food acceptability. Safety endpoints include adverse event recording. Treatment effects will be estimated using linear mixed-effects models accounting for treatment, period and sequence, with prespecified carryover sensitivity analyses. A total of 40 participants will be recruited to generate feasibility data and effect size estimates. This protocol will provide crossover evidence on whether pragmatic, product-level low-GI staple substitution improves TyG and related cardiometabolic and vascular biomarkers in adults with suboptimal triglyceridemia, informing larger trials. Trial registration: ClinicalTrials.gov NCT07198789. Full article

Event cameras provide microsecond-level temporal resolution, low latency, and high dynamic range, enabling robust perception under fast motion and challenging lighting conditions. Nevertheless, event streams are susceptible to background activity, thermal noise, and hot pixels. Their sparse and irregular patterns can corrupt event structures and degrade downstream tasks. We propose MSF, a multi-level spatiotemporal filtering framework that couples motion-compensated aggregation with neighborhood-level verification. In each temporal window, MSF estimates a constant 2D optical flow by maximizing a robust, density-normalized contrast objective on the image of warped events (IWE). We further incorporate polarity–gradient decorrelation to suppress mixed-polarity noise and an explicit peak-suppression regularizer to avoid hot-pixel-induced degeneracy. The motion parameters are optimized via coarse grid initialization followed by gradient-ascent refinement. Based on the estimated motion, MSF performs hierarchical event selection: central events are extracted from high-confidence aggregated regions, local events are recovered through joint spatial–temporal–directional–polarity consistency, and weak border events are identified using a density-normalized probabilistic support model that rewards support from reliable structures while penalizing self-clustering. Experiments on four public benchmarks (DVSNOISE20, DVSMOTION20, DVSCLEAN, and E-MLB) show that MSF consistently improves the Event Structural Ratio (ESR) and outperforms representative baselines across diverse motion regimes and severe low-light noise. Full article

The formation of recyclable polyethylene materials is significantly limited by traditional crosslinking methods, which involve solvent-heavy processes and permanent chemical bonds that cannot be undone. Herein, we report an environmentally friendly and scalable approach to construct a thermo-reversible polyethylene network (PE-g-DA) via solvent-free, one-step melt processing based on furan–maleimide Diels–Alder (D–A) dynamic covalent chemistry. Furan-functionalized polyethylene was dynamically crosslinked with bismaleimide during melt mixing, fully compatible with conventional polyolefin processing techniques. FTIR spectroscopy, temperature-dependent solubility, and differential scanning calorimetry collectively confirm the reversible formation and dissociation of D–A adducts, enabling thermal switching of the network structure. Equilibrium swelling experiments based on the Flory–Rehner model indicate that the crosslink density can be precisely controlled by varying the bismaleimide content. As a result, PE-g-DA exhibits significantly enhanced tensile strength while maintaining high ductility at moderate crosslink densities. Notably, the dynamic network allows efficient thermal reprocessing, with recycled samples retaining approximately 93% and 80% of their original tensile strength after the first and second reprocessing cycles, respectively. Moreover, intrinsic thermal self-healing behavior is directly visualized by scanning electron microscopy at 120 °C. This work demonstrates that combining dynamic Diels–Alder chemistry with solvent-free melt processing offers a practical and sustainable route to recyclable, reprocessable, and self-healable polyethylene materials with clear potential for large-scale industrial production. Full article